Mapping scRNA-seq based cell predictions onto Spatial Transcriptomic data
Larry Chen
2023-01-18
NOTE: This code utilizes Seurat v4.1.1. You will need to install seurat version 4.1.1 using ‘remotes’. We recommend installing the version in separate location from your regular seurat package.
remotes::install_version('Seurat', version = '4.1.1', lib = "C:/Users/Ji Lab/AppData/Local/R/alt_packages/Seurat 4.1.1" )Import Libraries
library(Seurat, lib.loc = "C:/Users/Ji Lab/AppData/Local/R/alt_packages/Seurat 4.1.1")
library(dplyr)
library(ggplot2)
library(patchwork)sessioninfo::session_info()%>%
details::details(
summary = 'Current session info',
open = TRUE
)Current session info
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.2.2 (2022-10-31 ucrt)
os Windows 10 x64 (build 22621)
system x86_64, mingw32
ui RTerm
language (EN)
collate English_United States.utf8
ctype English_United States.utf8
tz America/New_York
date 2023-01-19
pandoc 2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0)
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cli 3.4.1 2022-09-23 [1] CRAN (R 4.2.1)
clipr 0.8.0 2022-02-22 [1] CRAN (R 4.2.1)
cluster 2.1.4 2022-08-22 [2] CRAN (R 4.2.2)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.2.2)
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cowplot 1.1.1 2020-12-30 [1] CRAN (R 4.2.1)
data.table 1.14.6 2022-11-16 [1] CRAN (R 4.2.2)
DBI 1.1.3 2022-06-18 [1] CRAN (R 4.2.1)
deldir 1.0-6 2021-10-23 [1] CRAN (R 4.2.0)
desc 1.4.2 2022-09-08 [1] CRAN (R 4.2.1)
details 0.3.0 2022-03-27 [1] CRAN (R 4.2.2)
digest 0.6.30 2022-10-18 [1] CRAN (R 4.2.1)
dplyr * 1.0.10 2022-09-01 [1] CRAN (R 4.2.1)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.1)
evaluate 0.19 2022-12-13 [1] CRAN (R 4.2.2)
fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.1)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.1)
fitdistrplus 1.1-8 2022-03-10 [1] CRAN (R 4.2.1)
future 1.30.0 2022-12-16 [1] CRAN (R 4.2.2)
future.apply 1.10.0 2022-11-05 [1] CRAN (R 4.2.2)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1)
ggplot2 * 3.4.0 2022-11-04 [1] CRAN (R 4.2.2)
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ggridges 0.5.4 2022-09-26 [1] CRAN (R 4.2.1)
globals 0.16.2 2022-11-21 [1] CRAN (R 4.2.2)
glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1)
goftest 1.2-3 2021-10-07 [1] CRAN (R 4.2.0)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.1)
gtable 0.3.1 2022-09-01 [1] CRAN (R 4.2.1)
htmltools 0.5.4 2022-12-07 [1] CRAN (R 4.2.2)
htmlwidgets 1.6.0 2022-12-15 [1] CRAN (R 4.2.2)
httpuv 1.6.6 2022-09-08 [1] CRAN (R 4.2.2)
httr 1.4.4 2022-08-17 [1] CRAN (R 4.2.1)
ica 1.0-3 2022-07-08 [1] CRAN (R 4.2.1)
igraph 1.3.5 2022-09-22 [1] CRAN (R 4.2.1)
irlba 2.3.5.1 2022-10-03 [1] CRAN (R 4.2.2)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.1)
jsonlite 1.8.3 2022-10-21 [1] CRAN (R 4.2.2)
KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.2)
knitr 1.41 2022-11-18 [1] CRAN (R 4.2.2)
later 1.3.0 2021-08-18 [1] CRAN (R 4.2.1)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.2.2)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.2.1)
leiden 0.4.3 2022-09-10 [1] CRAN (R 4.2.1)
lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1)
listenv 0.9.0 2022-12-16 [1] CRAN (R 4.2.2)
lmtest 0.9-40 2022-03-21 [1] CRAN (R 4.2.1)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1)
MASS 7.3-58.1 2022-08-03 [2] CRAN (R 4.2.2)
Matrix 1.5-4 2022-11-14 [1] R-Forge (R 4.2.2)
matrixStats 0.63.0 2022-11-18 [1] CRAN (R 4.2.2)
mgcv 1.8-41 2022-10-21 [2] CRAN (R 4.2.2)
mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.2.1)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.1)
nlme 3.1-160 2022-10-10 [2] CRAN (R 4.2.2)
parallelly 1.33.0 2022-12-14 [1] CRAN (R 4.2.2)
patchwork * 1.1.2 2022-08-19 [1] CRAN (R 4.2.1)
pbapply 1.6-0 2022-11-16 [1] CRAN (R 4.2.1)
pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.1)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.1)
plotly 4.10.1 2022-11-07 [1] CRAN (R 4.2.2)
plyr 1.8.8 2022-11-11 [1] CRAN (R 4.2.2)
png 0.1-8 2022-11-29 [1] CRAN (R 4.2.2)
polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.2.1)
progressr 0.12.0 2022-12-13 [1] CRAN (R 4.2.2)
promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.1)
purrr 0.3.5 2022-10-06 [1] CRAN (R 4.2.1)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.1)
RANN 2.6.1 2019-01-08 [1] CRAN (R 4.2.1)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.1)
RcppAnnoy 0.0.20 2022-10-27 [1] CRAN (R 4.2.2)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.2.1)
reticulate 1.26-9000 2022-11-29 [1] Github (rstudio/reticulate@a1d7f7f)
rlang 1.0.6 2022-09-24 [1] CRAN (R 4.2.1)
rmarkdown 2.19 2022-12-15 [1] CRAN (R 4.2.2)
ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.2.1)
rpart 4.1.19 2022-10-21 [2] CRAN (R 4.2.2)
rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.1)
rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.1)
Rtsne 0.16 2022-04-17 [1] CRAN (R 4.2.1)
sass 0.4.4 2022-11-24 [1] CRAN (R 4.2.2)
scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.1)
scattermore 0.8 2022-02-14 [1] CRAN (R 4.2.1)
sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.2.2)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.1)
VP Seurat * 4.1.1 2022-11-18 [?] CRAN (R 4.2.2) (on disk 4.3.0)
SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.2.2)
shiny 1.7.4 2022-12-15 [1] CRAN (R 4.2.2)
sp 1.5-1 2022-11-07 [1] CRAN (R 4.2.2)
spatstat.core 2.4-4 2022-05-18 [1] CRAN (R 4.2.1)
spatstat.data 3.0-0 2022-10-21 [1] CRAN (R 4.2.2)
spatstat.geom 3.0-3 2022-10-25 [1] CRAN (R 4.2.2)
spatstat.random 3.0-1 2022-11-03 [1] CRAN (R 4.2.2)
spatstat.sparse 3.0-0 2022-10-21 [1] CRAN (R 4.2.2)
spatstat.utils 3.0-1 2022-10-19 [1] CRAN (R 4.2.2)
stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.1)
stringr 1.5.0 2022-12-02 [1] CRAN (R 4.2.2)
survival 3.4-0 2022-08-09 [2] CRAN (R 4.2.2)
tensor 1.5 2012-05-05 [1] CRAN (R 4.2.0)
tibble 3.1.8 2022-07-22 [1] CRAN (R 4.2.1)
tidyr 1.2.1 2022-09-08 [1] CRAN (R 4.2.1)
tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.1)
utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.1)
uwot 0.1.14 2022-08-22 [1] CRAN (R 4.2.1)
vctrs 0.5.1 2022-11-16 [1] CRAN (R 4.2.2)
viridisLite 0.4.1 2022-08-22 [1] CRAN (R 4.2.1)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.1)
xfun 0.35 2022-11-16 [1] CRAN (R 4.2.2)
xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.1)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.1)
yaml 2.3.6 2022-10-18 [1] CRAN (R 4.2.1)
zoo 1.8-11 2022-09-17 [1] CRAN (R 4.2.2)
[1] C:/Users/Ji Lab/AppData/Local/R/win-library/4.2
[2] C:/Program Files/R/R-4.2.2/library
V ── Loaded and on-disk version mismatch.
P ── Loaded and on-disk path mismatch.
──────────────────────────────────────────────────────────────────────────────
Load ST object
n23_p1 = readRDS('C:/Users/Ji Lab/Documents/JID manuscript/andrew_scripts/orig_obj/st/n23_p1.Rds')Load Predictions
From n23_p1_seurat_prediction_merged_confident_CT_020621.Rmd
# Load in ns_int predictions
n23p1_prediction.ns_int = read.table("C:/Users/Ji Lab/Documents/JID manuscript/andrew_scripts/orig_obj/st/n23p1_predictions_ns_int_res0.6_ref.txt",
sep = "\t",row.names = 1,header=T,stringsAsFactors = F)
#filter out predictions for spots that were removed
n23_p1_prediction.ns_int_filtered = n23p1_prediction.ns_int[match(colnames(n23_p1),row.names(n23p1_prediction.ns_int)),]n23_p1$predicted.id = n23_p1_prediction.ns_int_filtered$predicted.id
Idents(n23_p1) = 'predicted.id'
SpatialDimPlot(n23_p1, images = "rep2")
Feature and Violin Plots
for (i in 2:42)
{
id_title = colnames(n23p1_prediction)[i]
n23_p1@meta.data$pred = as.numeric(n23p1_prediction[,i])
p = SpatialFeaturePlot(n23_p1,features = "pred")
png(paste("n23p1_stfeatureplot_",id_title,".png",sep=""),width = 12, height=4, units = "in", res = 300)
print(p)
dev.off()
p2 = VlnPlot(n23_p1,features = "pred",group.by = "SCT_snn_res.0.8")
pdf(paste("n23p1_vlnplot_res0.8_",id_title,".pdf",sep=""),width = 6, height=4)
print(p2)
dev.off()
}## [1] "prediction.score.ns_int_res0.6_C2"
## [1] "prediction.score.ns_int_res0.6_C4"
## [1] "prediction.score.ns_int_res0.6_C20"
Add IFE/PSU designations to object
IFE/PSU designations made based on histology
#############
# Check IFE PSU
# Get IFE/PSU designations and add to each object
n23_p1 = subset(n23_p1, nCount_Spatial > 200)
spot_pos1 = read.table(paste(vis.dir,"IFE_PSU.csv",sep=""),sep = ",",row.names = 1,header = T,stringsAsFactors = F)
spot_pos2 = read.table(paste(vis.dir2,"IFE_PSU.csv",sep=""),sep = ",",row.names = 1,header = T,stringsAsFactors = F)
pos1_names = paste("rep1_",rownames(spot_pos1),sep="")
pos2_names = paste("rep2_",rownames(spot_pos2),sep="")
rownames(spot_pos1) = pos1_names
rownames(spot_pos2) = pos2_names
n23_p1_ifepsu = rep("Other",ncol(n23_p1))
n23_p1_ifepsu_rep1_match = match(rownames(spot_pos1),colnames(n23_p1))
n23_p1_ifepsu_rep1_match_filt = n23_p1_ifepsu_rep1_match[which(!is.na(n23_p1_ifepsu_rep1_match))]
n23_p1_ifepsu[n23_p1_ifepsu_rep1_match_filt] = spot_pos1[,1]
n23_p1_ifepsu_rep2_match = match(rownames(spot_pos2),colnames(n23_p1))
n23_p1_ifepsu_rep2_match_filt = n23_p1_ifepsu_rep2_match[which(!is.na(n23_p1_ifepsu_rep2_match))]
n23_p1_ifepsu[n23_p1_ifepsu_rep2_match_filt] = spot_pos2[,1]
n23_p1$IFE_PSU = n23_p1_ifepsuIdents(n23_p1) = 'IFE_PSU'
png(paste("n23_p1_IFE_PSU_check.png",sep=""), units = "in", height = 5, width = 10, res=300)
p=SpatialDimPlot(n23_p1)
print(p)
dev.off()